chaospy 3.2.0

Numerical tool for perfroming uncertainty quantification

Chaospy is a numerical tool for performing uncertainty quantification using polynomial chaos expansions and advanced Monte Carlo methods implemented in Python 2 and 3.

If you are using this software in work that will be published, please cite the journal article: Chaospy: An open source tool for designing methods of uncertainty quantification

Table of Contents:

• Installation

• Example Usage

• Related Projects

• Questions & Troubleshooting

Installation

Installation should be straight forward:

pip install chaospy

And you should be ready to go.

Alternatively, to get the most current experimental version, the code can be installed from Github as follows:

git clone [e-mail]:jonathf/chaospy.git
cd chaospy
git checkout <tag or branch of interest>
pip install .

Example Usage

chaospy is created to be simple and modular. A simple script to implement point collocation method will look as follows:

import chaospy
import numpy

# your code wrapper goes here
def foo(coord, prm):
    """Function to do uncertainty quantification on."""
    return prm[0] * numpy.e ** (-prm[1] * numpy.linspace(0, 10, 100))

# bi-variate probability distribution
distribution = chaospy.J(chaospy.Uniform(1, 2), chaospy.Uniform(0.1, 0.2))

# polynomial chaos expansion
polynomial_expansion = chaospy.orth_ttr(8, distribution)

# samples:
samples = distribution.sample(1000)

# evaluations:
evals = [foo(sample) for sample in samples.T]

# polynomial approximation
foo_approx = chaospy.fit_regression(
    polynomial_expansion, samples, evals)

# statistical metrics
expected = chaospy.E(foo_approx, distribution)
deviation = chaospy.Std(foo_approx, distribution)

For a more extensive description of what going on, see the tutorial.

For a collection of recipes, see the cookbook.

Related Projects

Chaospy is being used in other related projects that requires uncertainty quantification components chaospy provides.

easyVVUQ	Library designed to facilitate verification, validation and uncertainty quantification.
STARFiSh	Shell-based, scientific simulation program for blood flow in mammals.
Profit	Probabilistic response model fitting via interactive tools.
UncertainPy	Uncertainty quantification and sensitivity analysis, tailored towards computational neuroscience.

Also a few shout-outs:

OpenTURNS	Thanks to Régis Lebrun for both proposing a collaboration and creating an initial implementation of both Chaospy Compatibility in OpenTURNS and OpenTURNS Compatibility in chaospy.
orthopy quadpy	Thanks to Nico Schlömer for providing the implementation for several of the quadrature integration methods.
UQRF	Thanks to Florian Künzner for providing the implementation for sample distribution.

Questions & Troubleshooting

For any problems and questions you might have related to chaospy, please feel free to file an issue.